// -----

// CHECK-LABEL: float_matmul
func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

# CHECK-LABEL: func @main
# CHECK-SAME:  ([[VAL_0:%.*]]: tensor<1x8x8x2xi32>) -> (tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "unranked"
# CHECK-SAME:  outputs = "unranked,static,static_10"
# CHECK-DAG:         [[VAL_2:%.*]] = arith.constant dense<0> : tensor<10xi32>
# CHECK-DAG:         [[VAL_1:%.*]] = arith.constant dense<0> : tensor<i32>

// CHECK: shape
// CHECK: dim
// CHECK: size: 10

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>

func.func @dequantize_pushdown(%arg0: tensor<1000x1000x!quant.uniform<i8:f32, 7.812500e-03>>, %arg1: tensor<1x1xi32>) -> tensor<1x1x1000xf32> {
  %0 = "tfl.dequantize"(%arg0) : (tensor<1000x1000x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000x1000xf32>
  %1 = "tfl.gather"(%0, %arg1) {axis = 0 : i32, batch_dims = 0 : i32}: (tensor<1000x1000xf32>, tensor<1x1xi32>) -> tensor<1x1x1000xf32>
  func.return %1 : tensor<1x1x1000xf32>

// CHECK-NEXT: tfl.gather

  %0 = "mhlo.broadcast_in_dim"(%arg1) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xi32>) -> tensor<1x100xi32>
  %1 = "mhlo.select"(%arg0, %0, %arg2) : (tensor<i1>, tensor<1x100xi32>, tensor<1x100xi32>) -> tensor<1x100xi32>
  func.return %1 : tensor<1x100xi32>
}

// CHECK-LABEL:   func @selectv2_broadcasted_condition(

    } : (tensor<?x100352xf32>, tensor<100352x10xf32>) -> tensor<?x10xf32>
    %3 = "tf.Identity"(%2) {device = ""} : (tensor<?x10xf32>) -> tensor<?x10xf32>
    return %3 : tensor<?x10xf32>
  }

  // CHECK-LABEL: func.func private @composite_dot_general_fn_1
  func.func private @composite_dot_general_fn_1(%arg0: tensor<?x100352xf32>, %arg1: tensor<100352x10xf32>) -> tensor<?x10xf32> {
    // CHECK-NOT: tf.CustomAggregator

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} {
  %graph = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"

// -----

func.func @constant_tensor_invalid_3(%arg0: vector<1xi32>) -> tensor<1x1xi32> {
    // expected-error@+1 {{input and output should have same shape and element type}}
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1xi32>) -> tensor<1x1xi32>
  func.return %0 : tensor<1x1xi32>
}

// -----

func.func @constant_tensor_invalid_4(%arg0: i32) -> tensor<1x1xi32> {
    // expected-error@+1 {{input can not be converted to an output tensor}}

    // CHECK-NOT: tf.Multinomial
    %samples = "tf.Const"() { value = dense<10> : tensor<i32> } : () -> tensor<i32>
    %1 = "tf.Multinomial"(%arg0, %samples) {seed = 0, seed2 = 0}: (tensor<2x4xf32>, tensor<i32>) -> tensor<2x10xi32>
    func.return %1 : tensor<2x10xi32>
  }

  // CHECK-LABEL: @set_dynamic_dimension_size

// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>

func.func @cast_bf16_matmul_to_fp32(%arg0: tensor<1x10xf32>) -> (tensor<1x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<10x2xbf16>} : () -> tensor<10x2xbf16>
  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x10xf32>) -> tensor<1x10xbf16>